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Machine Learning-Based Predictive Farmland Optimization and Crop Monitoring System
Marion Olubunmi Adebiyi1, Roseline Oluwaseun Ogundokun1, Aneoghena Amarachi Abokhai1
1Department of Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria.
Scientifica
|May 27, 2020
Summary
This study introduces an e-agriculture mobile system using machine learning for farmland optimization. The system analyzes crop features to provide users with tailored optimization sets, improving farm management decisions.
Area of Science:
- Agricultural Science
- Computer Science
- Data Science
Background:
- E-agriculture integrates technology into farming for enhanced efficiency.
- Optimizing farmland requires analyzing complex datasets like soil type, pH, and crop features.
Purpose of the Study:
- To develop a machine learning-aided mobile system for farmland optimization.
- To assist farmers in making informed decisions for improved crop yields.
Main Methods:
- Utilized the Random Forest algorithm and BigML for data analysis and classification.
- Developed a mobile application using Appery.io to process user inputs and provide optimization sets.
Main Results:
- Generated subclasses based on random crop features, grouped into three main classes.
- The system successfully provided various optimization sets based on user input parameters.
- Demonstrated improved information optimization for users implementing the system on their farmlands.
Conclusions:
- The developed system effectively aids decision-making in farmland management.
- The mobile application offers practical optimization solutions for farmers.
- The approach enhances the utilization of agricultural data for better outcomes.
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